Optimization of liquid handling parameters for viscous liquid transfers with pipetting robots, a “sticky situation”

Quijano Velasco, Pablo; Low, Kai Yuan Andre; Leong, Chang Jie; Ng, Wan Ting; Qiu, Selina; Jhunjhunwala, Shivam; Li, Bryant; Qian, Anne; Hippalgaonkar, Kedar; Cheng, Jayce Jian Wei

doi:10.1039/d3dd00255a

Cited by 2 publications

(1 citation statement)

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“…It remains an open challenge to achieve accurate transfers of highly viscous, particularly non-Newtonian, fluids on a pipetting robot. Some of our group recently performed a study into closed-loop optimisation of liquid handling parameters for viscous handling, however, this work only goes up to 1275 mPa·s 34 . Instead, we found roughly optimal liquid handling parameters for each class of ingredient (surfactant/polymer/thickener) and retrofitted the OT-2 with a precision balance, as shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset

Chitre,

Querimit,

Rihm

et al. 2024

Sci Data

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Liquid formulations are ubiquitous yet have lengthy product development cycles owing to the complex physical interactions between ingredients making it difficult to tune formulations to customer-defined property targets. Interpolative ML models can accelerate liquid formulations design but are typically trained on limited sets of ingredients and without any structural information, which limits their out-of-training predictive capacity. To address this challenge, we selected eighteen formulation ingredients covering a diverse chemical space to prepare an open experimental dataset for training ML models for rinse-off formulations development. The resulting design space has an over 50-fold increase in dimensionality compared to our previous work. Here, we present a dataset of 812 formulations, including 294 stable samples, which cover the entire design space, with phase stability, turbidity, and high-fidelity rheology measurements generated on our semi-automated, ML-driven liquid formulations workflow. Our dataset has the unique attribute of sample-specific uncertainty measurements to train predictive surrogate models.

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Section: Methodsmentioning

confidence: 99%

Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset

Chitre,

Querimit,

Rihm

et al. 2024

Sci Data

Self Cite

View full text Add to dashboard Cite

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Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science

Cheng,

Ser,

Skreta

et al. 2025

Faraday Discuss.

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Optimization of liquid handling parameters for viscous liquid transfers with pipetting robots, a “sticky situation”

Abstract: Automated air-displacement pipettes have become a standard equipment for the transfer of liquids in laboratory settings. However, these tools fail to perform accurate and precise transfers for liquids with viscosities...

Cited by 2 publications

References 12 publications

Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset

Accelerating Formulation Design via Machine Learning: Generating a High-throughput Shampoo Formulations Dataset

Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science

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